A Deeper Dive Into Large Language Models | Generative AI Series

Introduction

Large Language Models (LLMs) are cutting-edge neural networks engineered to comprehend and generate human-like text. These models, such as GPT and Llama, boast enormous parameter counts, endowing them with remarkable language understanding capabilities. These models are constructed on transformer architectures, employing attention mechanisms for context processing.

LLMs undergo two key phases: pre-training on vast text corpora and fine-tuning for specific tasks. While they excel in natural language tasks like translation and sentiment analysis, challenges such as ethical concerns, bias mitigation, and model interpretability must be addressed. LLMs represent the forefront of natural language processing, driving innovations in AI-driven language understanding and generation.

This section introduces you to the inner workings of LLMs.

Definition of a Large Language Model

A large language model (LLM) is a deep learning algorithm that processes a range of natural language processing (NLP) tasks like text generation and classification, conversational questions and answers, and text translation.

Large language models (LLMs) train on massive datasets of text and code. These datasets contain trillions of words, and their quality impacts the model's performance.

The number of parameters in each LLM is also essential. For example, Llama, a popular LLM from Meta, is available in three flavors: 7B parameters, 13B parameters, and 70B parameters.

These parameters represent the units of memory that the model learns during training. You can think of these parameters as the knowledge base of the model. The larger the training dataset, the larger the size of the parameters. Because LLMs can potentially have billions or even trillions of parameters, they demand a lot of computing power to train and run.

After training an LLM, it can accept a prompt and respond with an output based on word completion, chat completion, translation, or even rephrase text.

Examples of LLMs

• GPT (Generative Pretrained Transformer 4) from OpenAI
• PaLM from Google
• Claude from Anthropic
• Llama from Meta
• Falcon from TII
• MPT from MosiacML
• Megatron-Turing by NVIDIA

Some models above are proprietary and available only through an API. Models such as Llama and Falcon are open and you can deploy them in a cloud environment. Later, you'll deploy some of these open-source models on the Vultr GPU Stack.

Key Characteristics of LLMs

At a high level, LLMs are text-based generative AI models that accept a prompt or a question and generate a response based on that.

Here are the key characteristics of LLMs:

Massive Scale

LLMs are enormous neural networks, typically composed of hundreds of millions to billions of parameters. Their immense size allows them to store and manipulate vast amounts of information, making them highly expressive.

Pretraining and Fine-Tuning

LLMs are pre-trained on extensive datasets containing text from the internet. During pretraining, they learn to predict the next word in a sentence, acquiring a broad understanding of language and world knowledge. After pretraining, they can be fine-tuned for specific tasks, making them versatile and adaptable.

Transformer Architecture

LLMs rely on the Transformer architecture that excels at handling sequential data. The Transformer architecture employs mechanisms that allow the model to weigh the importance of different words in a sentence, capturing complex relationships and dependencies.

Contextual Understanding

LLMs have a robust contextual understanding of language. They consider the context of a word or phrase when generating text, allowing them to produce coherent and contextually relevant responses. This ability to grasp context is important in their impressive language generation capabilities.

Wide Range of NLP Tasks

You can apply LLMs in diverse NLP tasks like language translation, text summarization, sentiment analysis, and question-answering. Their adaptability and strong language understanding make them valuable across various domains and applications.

These characteristics collectively make LLMs powerful tools for natural language understanding and generation, enabling advancements in NLP, chatbots, content generation, and more. However, they also raise ethical and computational concerns due to their scale and potential biases in their training data.

Internal Workings of LLMs

LLMs use a complex neural network architectures that include the following building blocks:

Tokens

Tokens are the fundamental units of texts. In English and many other languages, tokens are typically words, but they can also be subwords, characters, or other text units, depending on the tokenization strategy used.

For example, the sentence "I love ice cream" can be tokenized into the following word tokens:

["I", "love", "ice", "cream"]

Vectors

Computer systems understand numbers better than words or sentences. For this reason, this is where vectors come into play. Vectors are mathematical representations of text in a multi-dimensional space. In LLMs, vectors represent words, phrases, sentences, or documents.

Each word or token in a text can be associated with a vector representation. These vectors can have hundreds or even thousands of dimensions, with each dimension encoding some aspect of the word's meaning or context.

Embeddings

Embeddings refer to the process of mapping words or tokens to vectors. In the context of NLP, word embeddings or token embeddings are numerical representations of words or tokens.

These embeddings come from machine learning algorithms like Word2Vec, GloVe, FastText, or transformer-based models like BERT and GPT. These models map each word or token to a dense vector representation in a way that encodes semantic and contextual information.

For example, the word "cat" might be embedded into a vector like [0.2, 0.5, -0.1, ...], where each number represents a dimension capturing some aspect of the word's meaning and context.

Text embeddings aim to capture the semantic meaning of words or text units. This means that words with similar meanings will have similar vector representations. For example, in a good text embedding model, "cat" and "kitten" might be close in vector space because they are semantically related.

Embeddings convert tokens into vectors, allowing computers to process and understand textual data numerically, which is essential for various machine learning and deep learning tasks, including LLMs.

The Transformer Models

At their core, LLMs are neural networks with an intricate architecture called the Transformer. The Transformer design handles sequential data, making it ideal for tasks involving language. It consists of multiple layers, each with two main components: the self-attention mechanism and feedforward neural networks. They're called "transformers" because they're great at transforming data sequences.

Imagine working with a data sequence like a sentence in a text or time series data. A transformer model is like a super-smart machine that's good at understanding and working with these sequences.

The secret sauce of transformers is something called "attention." Think of it as the model's ability to focus on different parts of the input sequence. For example, when translating a sentence from English to French, it pays more attention to the relevant words in English while generating each word in French.

Transformers are usually deep models with many layers. Each layer processes the data differently, gradually learning more complex patterns. This depth helps them capture intricate relationships in the data. Unlike older models, transformers may process all sequence parts at once. They don't have to go through it step by step. This capability makes them super fast and efficient.

In the transformer architecture, self-attention is a crucial feature. It allows the model to weigh the importance of each word based on the context of the entire sentence. So, it knows that "bank" means different things in "river bank" and "bank account".

Transformers train on a massive dataset to learn more about language and world knowledge. Then, fine-tuning them allows them to perform specific tasks like translation, sentiment analysis, or chatbots. For instance, models like BERT, GPT-3, and others use transformer-based architecture, which is very good for processing NLP tasks.

Building Modern Applications Powered by LLMs

LLMs are fast becoming an integral part of modern applications. They enhance the user experience by bringing natural language understanding into the interface. This capability enables users to interact with applications through intelligent chatbots and highly contextual search.

Developers can take advantage of LLMs' power by using the APIs made available by commercial providers like OpenAI or Cohere that expose GPT-4 and Command. These models, however, are proprietary and cannot be hosted outside of the provider's environment.

Another option is to use open-source models with a licensing policy that allows commercial and research use. These models are typically available on Hugging Face, but you can also host them in a cloud environment. This course focuses on open-source models you can host on the Vultr Cloud GPU platform.

Irrespective of how you consume the LLMs, the workflow remains unchanged. To improve the accuracy of the LLMs' responses, developers must understand the fundamentals of prompt engineering and techniques.

In the next section of this series, you'll explore prompt engineering and build the first LLM-powered application based on an open-source model.